However, spectral observations suffer from a lot of costs. The most powerful spectroscopy survey — the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) has taken spectra of about 10 million stars in low resolution, and about 3 million in medium resolution with a four-meter telescope in the past 8 years. In contrast to spectral observations, photometric observations have much higher observational efficiency. Compared to LAMOST, the Javalambre Photometric Local Universe Survey (J-PLUS, [1]}) observed 223.6 million objects in its five-year observational campaign http://J-PLUS.es/datareleases/data_release_dr2. Therefore, many studies have been working on estimating stellar parameters from photometry-based data by constructing models of photometric observation results [2]}, [3]}, [4]}. Their modeling requires highly precise photometric data from a few surveys, while another mighty tool — machine learning — can reveal the distribution or pattern hidden inside the photometry with rougher data.
